The 6D pose of an object is an inherently continuous quantity.
Some works discretize the continuous pose space [1]}, [2]}, and formulate the problem as classification.
Others avoid discretization by representing the pose using, e.g., quaternions [3]}, or the axis-angle representation [4]}, [5]}.
Work outside the domain of pose estimation has also considered rotation matrices [6]}, or in a more general case parametric representations of affine transformations [7]}.
In these cases the problem is often formulated as regression.
The choice of rotation representation has a major impact on the performance of the estimation method.
